Project Group - 15

Members: Sören Burghardt (5861012), Sarah Blanc (5854830), Allan Guzmán (5718619), Lars van den Berg (5626668) & Simon Schreders (5878845).

Research Objective

What are the effects on aviation that COVID-19 caused at the Amsterdam Schiphol Airport and its most frequented destinations for freight and passenger transport?

When COVID-19 became a global pandemic in 2020, the containments plunged the global economy into an unprecedented recession. The magnitude of the economic shock was such that it resulted in unrecognisable data and a "data fog" that made it difficult to interpret and, more importantly, to predict. The last two years have been extraordinary, both for global economy and the humanity. While the COVID-19 pandemic now appears to be under control thanks to vaccination programmes, parts of the global economy are not yet fully recovered. As a result of the protective travel restrictions, the aviation sector has been hit harder than many others industries. In this project, our aim was to observe the impacts that covid has had on aviation and in particular, the impacts on Amsterdam Schiphol Airport and its most frequented destinations. This from the perspective of passenger and freight transport.

This research question is answered by answering similiar sub-research questions:

1. What is the overall passenger and cargo transport from Amsterdam Schiphol Airport 2019 and 2021?
2. What is the top 5 of frequent destinations for passenger transport from Amsterdam Schiphol Airport (2019)?
3. What is the top 5 of frequent destinations for cargo transport from Amsterdam Schiphol Airport (2019)?
4. What are the volumes of these top 5's in the last 20 years?
5. How did the COVID-19 cases develop in these countries in the last two years?
6. How did the vaccination rate for COVID-19 develop in these countries in the last two years?
7. What impact did the COVID-19 cases have on the passenger transport from Amsterdam Schiphol Airport?
8. What impact did the COVID-19 cases have on the cargo transport from Amsterdam Schiphol Airport?
9. What impact did the vaccination rate have on the passenger transport at Amsterdam Schiphol Airport?
10. What impact did the vaccination rate have on the cargo transport at Amsterdam Schiphol Airport?
11. What is the impact of COVID-19 on the other Dutch airports?

Extra: Are there other effects caused by COVID-19 (China comparison)?

Contribution Statement

Sören: Background research, coding, conceptualisation, visualisation, data analysis, data modelling & reporting.

Sarah: Background research, coding, conceptualisation, visualisation, data analysis, data modelling & reporting.

Allan: Background research, coding, conceptualisation, visualisation, data analysis, data modelling & reporting.

Lars: Background research, coding, conceptualisation, visualisation, data analysis, data modelling & reporting.

Simon: Background research, coding, conceptualisation, visualisation, data analysis, data modelling & reporting.

Data Used

COVID-19 cases in the world (Our World in Data)

Flight data passengers (Eu Stat)

Flight data freight (EU Stat)

OD Data for Airport Amsterdam (EU Stat)

Flight data passengers & cargo Dutch airports (CBS)

Chinese data (Worldbank)

Data Pipeline

All the data for flights from Amsterdam Airport Schiphol (EHAM) were grouped by country. This way two top 5 were created for both the volume of cargo and mail and the passangers. The countries from these two top 5's and the Netherlands were used to filter the COVID-19 cases, using the amount of confirmed new cases per million in a country.

As a first step, we are going to import all the librairies that we will need.

import pandas as pd
import numpy as np
import plotly as plt

import math
import scipy

import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go

import geopandas as gpd

import plotly.io as pio
import plotly.graph_objects as go   
import country_converter as coco
from plotly.subplots import make_subplots

import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"
pd.options.mode.chained_assignment = None

1. What is the overall passenger and cargo transport at Amsterdam Schiphol Airport 2019 and 2021 ?

# Import datasets
cargo = pd.read_csv("Data_Sets/avia_gor_nl__custom_3564729_monthly_linear.csv")
passengers = pd.read_csv("Data_Sets/avia_par_nl__custom_3564728_monthly_linear.csv")

# COVID-19 dataset
filepath = 'Data_Sets/owid-covid-data.csv'
covid_data = pd.read_csv(filepath, delimiter=',')

# Importing the world geometry data from geopandas library
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

# Setting up special cleaning list
country_code_fix = {'EL':'GR','AN':'BQ'}

def codes_correction(df,ISO_2):
    # This function has a data frame and a ISO2 countries list and outputs the same 
    # dataframe adding a ISO3 code and the country name for each unique
    df[ISO_2] = df[ISO_2].replace(to_replace= country_code_fix)
    df['iso_3_country'] = coco.convert(names = df[ISO_2], to= 'ISO3')
    df['country_2_name'] = coco.convert(names = df[ISO_2], to= 'name_short')
    return df

def column_splitting(df):
    # This function has a data frame as an input with airport and country codes
    # and as an output the splitting of those codes 
    df[["airp_country_1","airp_code_1","airp_country_2","airp_code_2"]] = df.airp_pr.str.split("_",expand=True)
    df[["Year","Month"]] = df.TIME_PERIOD.str.split("-",expand=True)
    return df

# Calling the above defined function
passengers = column_splitting(passengers)
cargo = column_splitting(cargo)

# Set up datasets for visualisation 
passengers_for_map = passengers.groupby(['Year','airp_country_2']).sum()
passengers_for_map_2019 = passengers_for_map.loc['2019',:].reset_index()
passengers_for_map_2021 = passengers_for_map.loc['2021',:].reset_index()
passengers_for_map_2019 = codes_correction(passengers_for_map_2019,'airp_country_2')
passengers_for_map_2021 = codes_correction(passengers_for_map_2021,'airp_country_2')

cargo_for_map = cargo.groupby(['Year','airp_country_2']).sum()
cargo_for_map_2019 = cargo_for_map.loc['2019',:].reset_index()
cargo_for_map_2021 = cargo_for_map.loc['2021',:].reset_index()
cargo_for_map_2019 = codes_correction(cargo_for_map_2019,'airp_country_2')
cargo_for_map_2021 = codes_correction(cargo_for_map_2021,'airp_country_2')

# Defining visualisation labels
passenger_labels = {
    'OBS_VALUE':'Total passengers',
    'country_2_name': 'Country',
    'iso_3_country': 'Country code'
}
cargo_labels = {
    'country_2_name':'Country',
    'OBS_VALUE': 'Total cargo [tons]',
    'iso_3_country': 'Country code'
}

# Visualisation of passengers merged datasets in 2019
map_data_passengers_2019 = world.merge(passengers_for_map_2019,left_on = 'iso_a3', right_on = 'iso_3_country', how = 'left')

fig = px.choropleth(map_data_passengers_2019, hover_name = 'country_2_name',locations= 'iso_3_country', \
    locationmode= 'ISO-3', color='OBS_VALUE',labels = passenger_labels)

fig.update_layout(
    title_text = 'Total passengers flying from Schiphol Airport in 2019',
    coloraxis_colorbar_title_text = 'Total passengers',
    geo= dict(
        showframe = False,
        showcoastlines = False,
        projection_type = 'equirectangular'
    )
)
fig.show()

# Visualisation of passengers merged datasets in 2021
map_data_passengers_2021 = world.merge(passengers_for_map_2021,left_on = 'iso_a3', right_on = 'iso_3_country', how = 'left')

fig = px.choropleth(map_data_passengers_2021, hover_name = 'country_2_name',locations= 'iso_3_country', \
    locationmode= 'ISO-3', color='OBS_VALUE',labels = passenger_labels)

fig.update_layout(
    title_text = 'Total passengers flying from Schiphol Airport in 2021',
    coloraxis_colorbar_title_text = 'Total passengers',
    geo= dict(
        showframe = False,
        showcoastlines = False,
        projection_type = 'equirectangular'
    )
)
fig.show()

# Visualisation of passengers merged datasets in 2019
fig = px.bar(map_data_passengers_2019.sort_values('OBS_VALUE',ascending= False).head(20),\
    x= 'country_2_name', y = 'OBS_VALUE',labels=passenger_labels)

fig.update_layout(
    title_text = 'Total passengers transported by air from Schiphol Airport in 2019',
)
fig.show()

# Visualisation of passengers merged datasets in 2021
fig = px.bar(map_data_passengers_2021.sort_values('OBS_VALUE',ascending= False).head(20),\
    x= 'country_2_name', y = 'OBS_VALUE',labels=passenger_labels)

fig.update_layout(
    title_text = 'Total passengers transported by air from Schiphol Airport in 2021',
)
fig.show()

# Visualisation of cargo merged datasets in 2019
map_data_cargo_2019 = world.merge(cargo_for_map_2019,left_on = 'iso_a3', right_on = 'iso_3_country', how = 'left')

fig = px.choropleth(map_data_cargo_2019, hover_name = 'country_2_name',locations= 'iso_3_country', \
    locationmode= 'ISO-3', color='OBS_VALUE',labels = cargo_labels)

fig.update_layout(
    title_text = 'Total tons transported by air from Schiphol Airport in 2019',
    geo= dict(
        showframe = False,
        showcoastlines = False,
        projection_type = 'equirectangular'
    )
)
fig.show()

# Visualisation of passengers merged datasets in 2021
map_data_cargo_2021 = world.merge(cargo_for_map_2021,left_on = 'iso_a3', right_on = 'iso_3_country', how = 'left')

fig = px.choropleth(map_data_cargo_2021, hover_name = 'country_2_name',locations= 'iso_3_country', \
    locationmode= 'ISO-3', color='OBS_VALUE',labels = cargo_labels)

fig.update_layout(
    title_text = 'Total tons transported by air from Schiphol Airport in 2021',
    geo= dict(
        showframe = False,
        showcoastlines = False,
        projection_type = 'equirectangular'
    )
)
fig.show()

# Visualisation of cargo merged datasets in 2019
fig = px.bar(map_data_cargo_2019.sort_values('OBS_VALUE',ascending= False).head(20),\
    x= 'country_2_name', y = 'OBS_VALUE',labels= cargo_labels)
fig.update_layout(
    title_text = 'Total tons transported by air from Schiphol Airport in 2019',
    geo= dict(
        showframe = False,
        showcoastlines = False,
        projection_type = 'equirectangular'
    )
)
fig.show()

# Visualisation of passengers merged datasets in 2021
fig = px.bar(map_data_cargo_2021.sort_values('OBS_VALUE',ascending= False).head(20),\
    x= 'country_2_name', y = 'OBS_VALUE',labels= cargo_labels)
fig.update_layout(
    title_text = 'Total tons transported by air from Schiphol Airport in 2021',
    geo= dict(
        showframe = False,
        showcoastlines = False,
        projection_type = 'equirectangular'
    )
)
fig.show()

2. What is the top 5 of frequent destinations for passenger transport from Amsterdam Schiphol Airport (2019)?

def top_n_year(df,n,year):
    # This function has a dataframe, a number of entries and a year as inputs and 
    # as output the dataframe filtered for the top n countries on a specific year
    topn = df.groupby(['Year',"airp_country_2"]).sum()
    topn = topn.loc[year,:].sort_values("OBS_VALUE", ascending= False).head(n).reset_index()
    cleaned = df[df["airp_country_2"].isin(topn["airp_country_2"])]
    return cleaned

# Call defined functions for passengers and cargo dataset
top_passengers = codes_correction(top_n_year(passengers,5,'2019'),'airp_country_2')
top_cargo = codes_correction(top_n_year(cargo,5,'2019'),'airp_country_2')

# Calling the top 5 countries for passenger transport

top_5_countries_passengers = top_passengers.country_2_name.unique()
for i in top_5_countries_passengers:
    print(i)

Germany
Spain
Italy
United Kingdom
United States

3. What is the top 5 of frequent destinations for cargo transport from Amsterdam Schiphol Airport (2019)?

# Calling the top 5 countries for cargo transport
top_5_countries_cargo = top_cargo.country_2_name.unique()
for i in top_5_countries_cargo:
    print(i)

United Arab Emirates
Brazil
China
Qatar
United States

4. What are the volumes of these top 5's in the last 20 years?

# Line chart for passenger data from top 5 countries
total_passengers = top_passengers.groupby(['airp_country_2','Year']).sum()
total_passengers = total_passengers.reset_index()
total_passengers = codes_correction(total_passengers,'airp_country_2')
fig = px.line(total_passengers, x='Year', y= 'OBS_VALUE' ,color='country_2_name',labels= passenger_labels, title = 'Passenger data for top 5 countries' )
fig.show()

The above graph shows the historical passenger data from the top 5 countries. The unprecedented decline that COVID-19 caused on the passenger data can be seen from 2020 onwards. Other historical events on the worlds stage like the World Trade Center bombing in 2001 or the world economic crisis in 2008 had barelly effect compared to the pandemic.

total_cargo = top_cargo.groupby(['airp_country_2','Year']).sum()
total_cargo = total_cargo.reset_index()
total_cargo = codes_correction(total_cargo,'airp_country_2')
fig = px.line(total_cargo, x='Year', y= 'OBS_VALUE' ,color='country_2_name',labels=cargo_labels, title = 'Cargo data for top 5 countries')
fig.show()

Compared to the passenger data, the cargo data contains less trends. Where as the amount of passengers dropped dramatically after the beginning of the pandemic, the cargo data increases after 2021. The decreases from 2021 to 2022 can be explained because the 2022 is not complete yet.

In this historical data the difference between the countries is also more clear. From 2000 onwards China has seen a huge growth of their flight cargo data to and from Amsterdam, where as the US has been a steady provider of cargo in Amsterdam.

5. How did the COVID-19 cases develop in these countries in the last two years?

# Select the columns of interest from the original dataset and filter it, also expand the date to accomodate year and month
columns_of_interest = ['new_cases_smoothed_per_million','people_fully_vaccinated_per_hundred','date','location']
filtered_covid_data = covid_data[columns_of_interest]
filtered_covid_data[['Year','Month','Day']] = filtered_covid_data.date.str.split("-",expand=True)

# Selecting the countries of interest for cargo and passenger transport
cargo_countries_of_interest =['China','United States','United Arab Emirates','Brazil','Qatar','Netherlands']
passenger_countries_of_interest = ['United Kingdom','Spain','Germany','Italy','United States','Netherlands']

# Group by year, month and location to calculate the mean for cargo transport
cargo_filtered_covid_data = filtered_covid_data[filtered_covid_data['location'].isin(cargo_countries_of_interest)]
cargo_filtered_covid_data = cargo_filtered_covid_data.groupby(['location','Year','Month']).mean()

# Resetting the index and creating new collum with Year and Month 
cargo_filtered_covid_data = cargo_filtered_covid_data.reset_index()
cargo_filtered_covid_data['date'] = cargo_filtered_covid_data['Year'] + ['-'] + cargo_filtered_covid_data['Month'] 

# Group by year, month and location to calculate the mean for cargo transport
passenger_filtered_covid_data = filtered_covid_data[filtered_covid_data['location'].isin(passenger_countries_of_interest)]
passenger_filtered_covid_data = passenger_filtered_covid_data.groupby(['location','Year','Month']).mean()

#Resetting the index and creating new collum with Year and Month 
passenger_filtered_covid_data = passenger_filtered_covid_data.reset_index()
passenger_filtered_covid_data['date'] = passenger_filtered_covid_data['Year'] + ['-'] + passenger_filtered_covid_data['Month']

# Calling visualization top 5 countries for passenger transport

fig = px.line(passenger_filtered_covid_data, x='date', y= 'new_cases_smoothed_per_million' ,color='location',
                title= 'New covid cases in the top 5 countries for passenger transport', labels={
                     'new_cases_smoothed_per_million': 'Covid cases per million',
                     'date' : 'Date',
                     'location' : 'Country'})

fig.show()

# Calling visualization top 5 countries for cargo transport

fig = px.line(cargo_filtered_covid_data, x='date', y= 'new_cases_smoothed_per_million', color='location',
              title= 'New covid cases in the top 5 countries for cargo transport', labels={
                     'new_cases_smoothed_per_million': 'Covid cases per million',
                     'date' : 'Date',
                     'location' : 'Country'}, range_y=[0,4000] )

fig.show()

6. How did the vaccination rate for COVID 19 develop in these countries in the last two years?

# Calling visualization top 5 countries for passenger transport

fig = px.line(passenger_filtered_covid_data, x='date', y= 'people_fully_vaccinated_per_hundred' ,color='location',
               title= 'Vaccination rate in percent in the top 5 countries for passenger transport', labels={
                     'people_fully_vaccinated_per_hundred' : 'People fully vaccinated (%)',
                     'date' : 'Date',
                     'location' : 'Country'})

fig.show()

# Chagne labels

# Calling visualization top 5 countries for cargo transport

fig = px.line(cargo_filtered_covid_data, x='date', y= 'people_fully_vaccinated_per_hundred' ,color='location',
                title= 'Vaccination rate in percent in the top 5 countries for cargo transport', labels={
                     'people_fully_vaccinated_per_hundred' : 'People fully vaccinated (%)',
                     'date' : 'Date',
                     'location' : 'Country'})

fig.show()

# Change labels

7. What impact did the COVID-19 cases have on the passenger transport from Amsterdam Schiphol Airport ?

#Data correction passengers
passengers_pre_merge = top_passengers.groupby(['airp_country_2','TIME_PERIOD','Month']).sum()
passengers_pre_merge = passengers_pre_merge.reset_index()
passengers_pre_merge = passengers_pre_merge[passengers_pre_merge['TIME_PERIOD'].between('2020-01', '2022-07')]
passengers_pre_merge = codes_correction(passengers_pre_merge,'airp_country_2')

#Data correction cargo
cargo_pre_merge = top_cargo.groupby(['airp_country_2','TIME_PERIOD','Month']).sum()
cargo_pre_merge = cargo_pre_merge.reset_index()
cargo_pre_merge = cargo_pre_merge[cargo_pre_merge['TIME_PERIOD'].between('2020-01', '2022-07')]
cargo_pre_merge = codes_correction(cargo_pre_merge,'airp_country_2')

#Data correction covid
passenger_filtered_covid_data = passenger_filtered_covid_data.reset_index()
cargo_filtered_covid_data = cargo_filtered_covid_data.reset_index()


merged_passengers = pd.merge(passenger_filtered_covid_data,passengers_pre_merge, left_on=('location','date'),right_on=('country_2_name','TIME_PERIOD'))
merged_cargo = pd.merge(cargo_filtered_covid_data,cargo_pre_merge, left_on=('location','date'),right_on=('country_2_name','TIME_PERIOD'))

# Passengers and Covid
fig = make_subplots(specs=[[{"secondary_y": True}]])
fig.add_trace(
    go.Scatter(x = merged_passengers[merged_passengers['location']== 'Germany']['date'],y = merged_passengers[merged_passengers['location']== 'Germany']['OBS_VALUE'], name='Germany flight'),
    secondary_y=False
)
fig.add_trace(
    go.Scatter(x = merged_passengers[merged_passengers['location']== 'Germany']['date'],y = merged_passengers[merged_passengers['location']== 'Germany']['new_cases_smoothed_per_million'], name='Germany covid'),
    secondary_y=True,
)
fig.add_trace(
    go.Scatter(x = merged_passengers[merged_passengers['location']== 'Italy']['date'],y = merged_passengers[merged_passengers['location']== 'Italy']['OBS_VALUE'], name='Italy flight'),
    secondary_y=False
)
fig.add_trace(
    go.Scatter(x = merged_passengers[merged_passengers['location']== 'Italy']['date'],y = merged_passengers[merged_passengers['location']== 'Italy']['new_cases_smoothed_per_million'], name='Italy covid'),
    secondary_y=True,
)
fig.add_trace(
    go.Scatter(x = merged_passengers[merged_passengers['location']== 'United States']['date'],y = merged_passengers[merged_passengers['location']== 'United States']['OBS_VALUE'], name='United States flight'),
    secondary_y=False
)
fig.add_trace(
    go.Scatter(x = merged_passengers[merged_passengers['location']== 'United States']['date'],y = merged_passengers[merged_passengers['location']== 'United States']['new_cases_smoothed_per_million'], name='United States covid'),
    secondary_y=True,
)
fig.add_trace(
    go.Scatter(x = merged_passengers[merged_passengers['location']== 'United Kingdom']['date'],y = merged_passengers[merged_passengers['location']== 'United Kingdom']['OBS_VALUE'], name='United Kingdom flight'),
    secondary_y=False
)
fig.add_trace(
    go.Scatter(x = merged_passengers[merged_passengers['location']== 'United Kingdom']['date'],y = merged_passengers[merged_passengers['location']== 'United Kingdom']['new_cases_smoothed_per_million'], name='United Kingdom covid'),
    secondary_y=True,
)
fig.add_trace(
    go.Scatter(x = merged_passengers[merged_passengers['location']== 'Spain']['date'],y = merged_passengers[merged_passengers['location']== 'Spain']['OBS_VALUE'], name='Spain flight'),
    secondary_y=False
)
fig.add_trace(
    go.Scatter(x = merged_passengers[merged_passengers['location']== 'Spain']['date'],y = merged_passengers[merged_passengers['location']== 'Spain']['new_cases_smoothed_per_million'], name='Spain covid'),
    secondary_y=True,
)

options = {'Germany':'Germany','Italy':'Italy','United Stated':'United States','United Kingdom':'United Kingdom','Spain':'Spain'}
options2 = ['All','Germany','Italy','United States','United Kingdom','Spain','Covid only','Flight only']
visList = [
    [True,True,True,True,True,True,True,True,True,True],
    [True,True,False,False,False,False,False,False,False,False],
    [False,False,True,True,False,False,False,False,False,False],
    [False,False,False,False,True,True,False,False,False,False],
    [False,False,False,False,False,False,True,True,False,False],
    [False,False,False,False,False,False,False,False,True,True],
    [False,True,False,True,False,True,False,True,False,True],
    [True,False,True,False,True,False,True,False,True,False]]
buttons = []
for i, g in enumerate(options2):
    button = dict(label =  g,
    method = 'restyle',
    args = ['visible',visList[i]])
    buttons.append(button)

fig.update_layout(
    xaxis_title='Date',
    yaxis_title='Number of passengers',
    title_text='Flight passengers compared to the covid cases per million',
    updatemenus=[
        dict(
            type='dropdown',
            direction='right',
            buttons = buttons,
            x = 0.13,
            y = 1.16,
            bordercolor = 'grey'   
        )
    ],
)
fig.update_yaxes(title_text='Covid cases per million people', secondary_y=True)
fig.show()

In the above graph the combination of COVID-19 new cases and the total amount of passengers transported from Amsterdam Airport Schiphol to the specific countries can be seen. Different combinations can be selected. In general when the amount of new COVID-19 cases are raising, the amount of passengers transported from Amsterdam Airport Schiphol to the country is decreased. For example, when looking at January 2022 in Italy, there's a peak in the amount of new COVID-19 cases and a valley in the amount of passengers transported.

Change labels of title, legend and y-axis

8. What impact did the COVID-19 cases have on the cargo transport from Amsterdam Schiphol Airport ?

# Cargo and Covid
fig = make_subplots(specs=[[{'secondary_y': True}]])
fig.add_trace(
    go.Scatter(x = merged_cargo[merged_cargo['location']== 'China']['date'],y = merged_cargo[merged_cargo['location']== 'China']['OBS_VALUE'], name='China flight'),
    secondary_y=False
)
fig.add_trace(
    go.Scatter(x = merged_cargo[merged_cargo['location']== 'China']['date'],y = merged_cargo[merged_cargo['location']== 'China']['new_cases_smoothed_per_million'], name='China covid'),
    secondary_y=True,
)
fig.add_trace(
    go.Scatter(x = merged_cargo[merged_cargo['location']== 'United Arab Emirates']['date'],y = merged_cargo[merged_cargo['location']== 'United Arab Emirates']['OBS_VALUE'], name='United Arab Emirates flight'),
    secondary_y=False
)
fig.add_trace(
    go.Scatter(x = merged_cargo[merged_cargo['location']== 'United Arab Emirates']['date'],y = merged_cargo[merged_cargo['location']== 'United Arab Emirates']['new_cases_smoothed_per_million'], name='United Arab Emirates covid'),
    secondary_y=True,
)
fig.add_trace(
    go.Scatter(x = merged_cargo[merged_cargo['location']== 'United States']['date'],y = merged_cargo[merged_cargo['location']== 'United States']['OBS_VALUE'], name='United States flight'),
    secondary_y=False
)
fig.add_trace(
    go.Scatter(x = merged_cargo[merged_cargo['location']== 'United States']['date'],y = merged_cargo[merged_cargo['location']== 'United States']['new_cases_smoothed_per_million'], name='United States covid'),
    secondary_y=True,
)
fig.add_trace(
    go.Scatter(x = merged_cargo[merged_cargo['location']== 'Brazil']['date'],y = merged_cargo[merged_cargo['location']== 'Brazil']['OBS_VALUE'], name='Brazil flight'),
    secondary_y=False
)
fig.add_trace(
    go.Scatter(x = merged_cargo[merged_cargo['location']== 'Brazil']['date'],y = merged_cargo[merged_cargo['location']== 'Brazil']['new_cases_smoothed_per_million'], name= 'Brazil covid'),
    secondary_y=True,
)
fig.add_trace(
    go.Scatter(x = merged_cargo[merged_cargo['location']== 'Qatar']['date'],y = merged_cargo[merged_cargo['location']== 'Qatar']['OBS_VALUE'], name='Qatar flight'),
    secondary_y=False
)
fig.add_trace(
    go.Scatter(x = merged_cargo[merged_cargo['location']== 'Qatar']['date'],y = merged_cargo[merged_cargo['location']== 'Qatar']['new_cases_smoothed_per_million'], name='Qatar covid'),
    secondary_y=True,
)

options2 = ['All','China','United Arab Emirates','United States','Brazil','Qatar','Covid only','Flight only']
visList = [
    [True,True,True,True,True,True,True,True,True,True],
    [True,True,False,False,False,False,False,False,False,False],
    [False,False,True,True,False,False,False,False,False,False],
    [False,False,False,False,True,True,False,False,False,False],
    [False,False,False,False,False,False,True,True,False,False],
    [False,False,False,False,False,False,False,False,True,True],
    [False,True,False,True,False,True,False,True,False,True],
    [True,False,True,False,True,False,True,False,True,False]]
buttons = []
for i, g in enumerate(options2):
    button = dict(label =  g,
    method = 'restyle',
    args = ['visible',visList[i]])
    buttons.append(button)

fig.update_layout(
    xaxis_title='Date',
    yaxis_title='Transported cargo [Tonnes]',
    title_text='Flight cargo compared to the covid cases per million',
    updatemenus=[
        dict(
            type='dropdown',
            direction='right',
            buttons = buttons,
            x = 0.17,
            y = 1.16,
            bordercolor = 'grey'   
        )
    ],
)
fig.update_yaxes(title_text='Covid cases per million people', secondary_y=True)
fig.show()

In the above graph the combination of COVID-19 new cases and the total amount of cargo transported from Amsterdam Airport Schiphol to the specific countries can be seen. Different combinations can be selected. In general when the amount of new COVID-19 cases are raising, the amount of cargo transported from Amsterdam Airport Schiphol to the country is decreased.

Change labels of title, legend and y-axis

9. What impact did the vaccination rate have on the passenger transport from Amsterdam Schiphol Airport ?

# Passengers and vaccination
fig = make_subplots(specs=[[{'secondary_y': True}]])
fig.add_trace(
    go.Scatter(x = merged_passengers[merged_passengers['location']== 'Germany']['date'],y = merged_passengers[merged_passengers['location']== 'Germany']['OBS_VALUE'], name='Germany flight'),
    secondary_y=False
)
fig.add_trace(
    go.Scatter(x = merged_passengers[merged_passengers['location']== 'Germany']['date'],y = merged_passengers[merged_passengers['location']== 'Germany']['people_fully_vaccinated_per_hundred'], name='Germany vaccinations'),
    secondary_y=True,
)
fig.add_trace(
    go.Scatter(x = merged_passengers[merged_passengers['location']== 'Italy']['date'],y = merged_passengers[merged_passengers['location']== 'Italy']['OBS_VALUE'], name='Italy flight'),
    secondary_y=False
)
fig.add_trace(
    go.Scatter(x = merged_passengers[merged_passengers['location']== 'Italy']['date'],y = merged_passengers[merged_passengers['location']== 'Italy']['people_fully_vaccinated_per_hundred'], name='Italy vaccinations'),
    secondary_y=True,
)
fig.add_trace(
    go.Scatter(x = merged_passengers[merged_passengers['location']== 'United States']['date'],y = merged_passengers[merged_passengers['location']== 'United States']['OBS_VALUE'], name='United States flight'),
    secondary_y=False
)
fig.add_trace(
    go.Scatter(x = merged_passengers[merged_passengers['location']== 'United States']['date'],y = merged_passengers[merged_passengers['location']== 'United States']['people_fully_vaccinated_per_hundred'], name='United States vaccinations'),
    secondary_y=True,
)
fig.add_trace(
    go.Scatter(x = merged_passengers[merged_passengers['location']== 'United Kingdom']['date'],y = merged_passengers[merged_passengers['location']== 'United Kingdom']['OBS_VALUE'], name='United Kingdom flight'),
    secondary_y=False
)
fig.add_trace(
    go.Scatter(x = merged_passengers[merged_passengers['location']== 'United Kingdom']['date'],y = merged_passengers[merged_passengers['location']== 'United Kingdom']['people_fully_vaccinated_per_hundred'], name='United Kingdom vaccinations'),
    secondary_y=True,
)
fig.add_trace(
    go.Scatter(x = merged_passengers[merged_passengers['location']== 'Spain']['date'],y = merged_passengers[merged_passengers['location']== 'Spain']['OBS_VALUE'], name='Spain flight'),
    secondary_y=False
)
fig.add_trace(
    go.Scatter(x = merged_passengers[merged_passengers['location']== 'Spain']['date'],y = merged_passengers[merged_passengers['location']== 'Spain']['people_fully_vaccinated_per_hundred'], name='Spain vaccinations'),
    secondary_y=True,
)

options = {'Germany':'Germany','Italy':'Italy','United Stated':'United States','United Kingdom':'United Kingdom','Spain':'Spain'}
options2 = ['All','Germany','Italy','United States','United Kingdom','Spain','Vaccinations only','Flight only']
visList = [
    [True,True,True,True,True,True,True,True,True,True],
    [True,True,False,False,False,False,False,False,False,False],
    [False,False,True,True,False,False,False,False,False,False],
    [False,False,False,False,True,True,False,False,False,False],
    [False,False,False,False,False,False,True,True,False,False],
    [False,False,False,False,False,False,False,False,True,True],
    [False,True,False,True,False,True,False,True,False,True],
    [True,False,True,False,True,False,True,False,True,False]]
buttons = []
for i, g in enumerate(options2):
    button = dict(label =  g,
    method = 'restyle',
    args = ['visible',visList[i]])
    buttons.append(button)

fig.update_layout(
    xaxis_title='Date',
    yaxis_title='Number of passengers',
    title_text='Flight passengers compared to the percentage of vaccinated people',
    updatemenus=[
        dict(
            type='dropdown',
            direction='right',
            buttons = buttons,
            x = 0.13,
            y = 1.16,
            bordercolor = 'grey'   
        )
    ],
)
fig.update_yaxes(title_text='Fully vaccinated people [%]' , secondary_y=True)
fig.show()

In the above graph the combination of the percentage of fully vaccinated people and the total amount of passengers transported from Amsterdam Airport Schiphol to the specific countries can be seen. Different combinations can be selected. For all countries can be included, when the amount of fully vaccinated people is raisng, the amount of passengers transported to the countries is raising too.

Change labels of title, legend and y-axis

10. What impact did the vaccination rate have on the cargo transport from Amsterdam Schiphol Airport ?

# Cargo and vaccination 
fig = make_subplots(specs=[[{'secondary_y': True}]])
fig.add_trace(
    go.Scatter(x = merged_cargo[merged_cargo['location']== 'China']['date'],y = merged_cargo[merged_cargo['location']== 'China']['OBS_VALUE'], name='China flight'),
    secondary_y=False
)
fig.add_trace(
    go.Scatter(x = merged_cargo[merged_cargo['location']== 'China']['date'],y = merged_cargo[merged_cargo['location']== 'China']['people_fully_vaccinated_per_hundred'], name='China vaccinations'),
    secondary_y=True,
)
fig.add_trace(
    go.Scatter(x = merged_cargo[merged_cargo['location']== 'United Arab Emirates']['date'],y = merged_cargo[merged_cargo['location']== 'United Arab Emirates']['OBS_VALUE'], name='United Arab Emirates flight'),
    secondary_y=False
)
fig.add_trace(
    go.Scatter(x = merged_cargo[merged_cargo['location']== 'United Arab Emirates']['date'],y = merged_cargo[merged_cargo['location']== 'United Arab Emirates']['people_fully_vaccinated_per_hundred'], name='United Arab Emirates vaccinations'),
    secondary_y=True,
)
fig.add_trace(
    go.Scatter(x = merged_cargo[merged_cargo['location']== 'United States']['date'],y = merged_cargo[merged_cargo['location']== 'United States']['OBS_VALUE'], name='United States flight'),
    secondary_y=False
)
fig.add_trace(
    go.Scatter(x = merged_cargo[merged_cargo['location']== 'United States']['date'],y = merged_cargo[merged_cargo['location']== 'United States']['people_fully_vaccinated_per_hundred'], name='United States vaccinations'),
    secondary_y=True,
)
fig.add_trace(
    go.Scatter(x = merged_cargo[merged_cargo['location']== 'Brazil']['date'],y = merged_cargo[merged_cargo['location']== 'Brazil']['OBS_VALUE'], name='Brazil flight'),
    secondary_y=False
)
fig.add_trace(
    go.Scatter(x = merged_cargo[merged_cargo['location']== 'Brazil']['date'],y = merged_cargo[merged_cargo['location']== 'Brazil']['people_fully_vaccinated_per_hundred'], name= 'Brazil vaccinations'),
    secondary_y=True,
)
fig.add_trace(
    go.Scatter(x = merged_cargo[merged_cargo['location']== 'Qatar']['date'],y = merged_cargo[merged_cargo['location']== 'Qatar']['OBS_VALUE'], name='Qatar flight'),
    secondary_y=False
)
fig.add_trace(
    go.Scatter(x = merged_cargo[merged_cargo['location']== 'Qatar']['date'],y = merged_cargo[merged_cargo['location']== 'Qatar']['people_fully_vaccinated_per_hundred'], name='Qatar vaccinations'),
    secondary_y=True,
)

options2 = ['All','China','United Arab Emirates','United States','Brazil','Qatar','Vaccinations only','Flight only']
visList = [
    [True,True,True,True,True,True,True,True,True,True],
    [True,True,False,False,False,False,False,False,False,False],
    [False,False,True,True,False,False,False,False,False,False],
    [False,False,False,False,True,True,False,False,False,False],
    [False,False,False,False,False,False,True,True,False,False],
    [False,False,False,False,False,False,False,False,True,True],
    [False,True,False,True,False,True,False,True,False,True],
    [True,False,True,False,True,False,True,False,True,False]]
buttons = []
for i, g in enumerate(options2):
    button = dict(label =  g,
    method = 'restyle',
    args = ['visible',visList[i]])
    buttons.append(button)

fig.update_layout(
    xaxis_title='Date',
    yaxis_title='Transported cargo [Tonnes]',
    title_text='Flight cargo compared to the percentage of people vaccinated',
    updatemenus=[
        dict(
            type='dropdown',
            direction='right',
            buttons = buttons,
            x = 0.15,
            y = 1.16,
            bordercolor = 'grey'   
        )
    ],
)
fig.update_yaxes(title_text='Fully vaccinated people [%]', secondary_y=True)
fig.show()

In the above graph the combination of the percentage of fully vaccinated people and the total amount of cargo transported from Amsterdam Airport Schiphol to the specific countries can be seen. Different combinations can be selected. The effect of fully vaccinated people in the country compared to the amount of cargo transport from Amsterdam Airport Schiphol to that country is not as visible, compared to the amount of passengers transported to that country.

Change labels of title, legend and y-axis

11. What is the impact of COVID 19 on the other Dutch airports?

# Changing the datasets for the other Dutch airpords

filepath = 'Data_Sets/Dutch Airports.csv'
DutchAirports = pd.read_csv(filepath, delimiter=';')
 
DutchAirports = DutchAirports.drop(['Aircraft movements/Cross-country flights (number)', 'Aircraft movements/Local flights (number)', 'Commercial air traffic/Flights/All flights/Scheduled (number)'], axis = 1)
DutchAirports = DutchAirports.set_index('Airports')
DutchAirports = DutchAirports.T
DutchAirports = DutchAirports.drop(['Total Dutch airports'], axis = 1)
DutchAirports = DutchAirports.T
DutchAirports = DutchAirports.reset_index()
DutchAirports = DutchAirports.set_index('Periods')
DutchAirports = DutchAirports.T
DutchAirports = DutchAirports.drop(['2018'], axis = 1)
DutchAirports = DutchAirports.drop(['2019'], axis = 1)
DutchAirports = DutchAirports.drop(['2020*'], axis = 1)
DutchAirports = DutchAirports.drop(['2021*'], axis = 1)
DutchAirports = DutchAirports.T
DutchAirports = DutchAirports.reset_index()
#DutchAirports = DutchAirports.set_index('Airports')
DutchAirports['PC_flights'] = DutchAirports['Commercial air traffic/Flights/All flights/Total flights (number)'].pct_change()*100
DutchAirports['PC_pass'] = DutchAirports['Commercial air traffic/Passengers/Total passengers/Total passengers (number)'].pct_change()*100
DutchAirports['PC_cargo'] = DutchAirports['Commercial air traffic/Cargo/Total cargo/Total cargo (ton)'].pct_change()*100
DutchAirports['PC_mail'] = DutchAirports['Commercial air traffic/Mail/Total mail/Total mail (ton)'].pct_change()*100
DutchAirports = DutchAirports.fillna(0)

# Plot interactive
plot = px.line(DutchAirports, x='Periods', y= 'PC_flights', color='Airports', title= 'Percentage of variation in the number of commercial air traffic flights',
                 labels={
                   # Change label names
                   'PC_flights': 'Variation [%]',
                   'Periods': 'Date'
               }, )

# Add range selector to zoom in on periods in Y axis.
plot.update_layout(
    xaxis=dict(
        rangeselector=dict(
            buttons=list([
                dict(count=1,
                     step="month",
                     stepmode="backward"),
            ])
        ),
        rangeslider=dict(
            visible=True
        ),
    )
)

plot.show()

# Plot interactive

fig = px.bar(DutchAirports, x='PC_flights', y= 'Airports', color='Airports', animation_frame='Periods', 
                 animation_group='Airports', orientation='h', title= 'Percentage of variation in the number of commercial air traffic flights',
                 labels={
                   # Change label names
                   'PC_flights': 'Variation [%]',
                   'date': 'Date'
               }, ) 

fig.update_layout(showlegend=False,xaxis_range = [-100,800])
#fig.update_layout(yaxis={'categoryorder':'total ascending'})
                 
# Show the graph
fig.show()

# Plot interactive (color makes possible to select on right of graph)
plot = px.line(DutchAirports, x='Periods', y= 'PC_cargo', color='Airports')

# Add range selector to zoom in on periods in Y axis.
plot.update_layout(
    xaxis=dict(
        rangeselector=dict(
            buttons=list([
                dict(count=1,
                     step="month",
                     stepmode="backward"),
            ])
        ),
        rangeslider=dict(
            visible=True
        ),
    )
)

plot.show()

From all these visualisations we can conclude that, in general, all airports in the Netherlands have suffered the same consequences from Covid. By going into more detail in each of the visualisations:

• Regarding the number of commercial air flights, increases can be observed during the summer periods due to the recovery of air traffic and the abandonment of the strict restrictive measures. It can be seen that the curves have generally the same shape and therefore the same trends.
• For commercial air flight passengers, the curves also have the same shapes. They follow the different waves of the epidemic and the different restrictive measures used to control the covid 19 epidemic. It is interesting to remove the airports with the largest numbers from the graph (by selecting them in the legend on the right hand side) each time in order to zoom in on the smaller airports. By doing this, it is clear that all the curves have the same appearance: a drop at the beginning of 2020, an increase in summer 2020, a drop between 2020 and 2021, an increase in summer 2021, a drop at the end of 2021 and then an increase until today.
• Finally, for the commercial air traffic cargo, only Schipol and Maastricht Airport have data. However, again, their curves have the same shape which again indicates that covid has had the same effects but on a different scale as the two airports are not at all the same size and therefore do not carry the same quantities of goods

Extra: COVID-data in China

While discussing the first research question we noticed that, even though is has been proven that COVID-19 arised in China, the amount of COVID cases are very low. We decided to investige this futher. For the first part of the visualisation, we decided to compare the impact on the transport sector of the start of Covid in the Netherlands and in China. In this way, it will be possible to compare the two situations in order to understand whether the Chinese have really been able to escape Covid and its impacts or not. Indeed, as their number of cases is officially so low, it would be possible to think that their economy has not been impacted. But is this really the case? What were the influences of the covid on the country compared to the Netherlands? We decided to proceed as follows:

• First, we will compare the purely covid-related data between the two countries.
• Then, we will analyse the behaviour of the transport economy in China in order to compare it to that of the Netherlands.

Quantities cannot be compared as such. After all, China is one of the world's largest powers, which is in no way comparable with the Netherlands. However, it is possible to compare the trend of the different curves when the first wave of Covid appeared (increasing, decreasing, stagnating).

# Import 

filepath = 'Data_Sets/owid-covid-data.csv'
covid_data = pd.read_csv(filepath, delimiter=',')

# Select the columns of interest from the original dataset and filter it, also expand the date to accomodate year and month
columns_of_interest = ['new_cases_smoothed_per_million','people_fully_vaccinated_per_hundred','date','location']
filtered_covid_data = covid_data[columns_of_interest]
filtered_covid_data[['Year','Month','Day']] = filtered_covid_data.date.str.split("-",expand=True)
filtered_covid_data.head()

#Select the countrys of interest 
Covid_China = filtered_covid_data[filtered_covid_data['location'].isin(['Netherlands','China'])]
Covid_China.head(-5)

	new_cases_smoothed_per_million	people_fully_vaccinated_per_hundred	date	location	Year	Month	Day
40988	NaN	NaN	2020-01-22	China	2020	01	22
40989	NaN	NaN	2020-01-23	China	2020	01	23
40990	NaN	NaN	2020-01-24	China	2020	01	24
40991	NaN	NaN	2020-01-25	China	2020	01	25
40992	NaN	NaN	2020-01-26	China	2020	01	26
...	...	...	...	...	...	...	...
141454	125.751	NaN	2022-10-02	Netherlands	2022	10	02
141455	125.735	NaN	2022-10-03	Netherlands	2022	10	03
141456	155.209	NaN	2022-10-04	Netherlands	2022	10	04
141457	155.209	NaN	2022-10-05	Netherlands	2022	10	05
141458	155.209	NaN	2022-10-06	Netherlands	2022	10	06

1947 rows × 7 columns

# Import the different datasets 

filepath1 = 'Data_Sets/Passengers.csv'
Passengers = pd.read_csv(filepath1, delimiter=',')

# Unuseful columns are removed
Passengers_drop = Passengers.drop(['Country Code', 'Indicator Name', 'Indicator Code'],  axis=1)
Passengers_drop.set_index('Country', inplace=True) 
Passengers_wanted = Passengers_drop.T

# Selection of the wanted data
China_passengers_final = Passengers_wanted.China.reset_index()
China_passengers_final.columns = ['Year', 'Number of passengers carried, China']
NL_passengers_final = Passengers_wanted.Netherlands.reset_index()
NL_passengers_final.columns = ['Year', 'Number of passengers carried, NL']

# Import of the data
filepath2 = 'Data_Sets/Freight.csv'
Freight = pd.read_csv(filepath2, delimiter=',')

# Unuseful columns are removed
Freight_drop = Freight.drop(['Country Code', 'Indicator Name', 'Indicator Code'],  axis=1)
Freight_drop.set_index('Country Name', inplace=True) 
Freight_wanted = Freight_drop.T

# Selection of the wanted data
NL_freight_final = Freight_wanted.Netherlands.reset_index()
NL_freight_final.columns = ['Year', 'Volume of freight carried, NL']
China_freight_final = Freight_wanted.China.reset_index()
China_freight_final.columns = ['Year', 'Volume of freight carried, China']

# Import of the data
filepath3 = 'Data_Sets/Container.csv'
Container = pd.read_csv(filepath3, delimiter=',')

# Unuseful columns are removed
Container_drop = Container.drop(['Country Code', 'Indicator Name', 'Indicator Code'],  axis=1)
Container_drop.set_index('Country Name', inplace=True) 
Container_wanted = Container_drop.T

# Selection of the wanted data
China_container_final = Container_wanted.China.reset_index()
China_container_final.columns = ['Year', 'Flow of containers carried, China']
NL_container_final = Container_wanted.Netherlands.reset_index()
NL_container_final.columns = ['Year', 'Flow of containers carried, NL']

# Import of the data
filepath4 = 'Data_Sets/CO2_emissions.csv'
CO2 = pd.read_csv(filepath4, delimiter=',')

# Unuseful columns are removed
CO2_drop = CO2.drop(['Country Code', 'Indicator Name', 'Indicator Code'],  axis=1)
CO2_drop.set_index('Country Name', inplace=True) 
CO2_wanted = CO2_drop.T

# Selection of the wanted data
NL_CO2_final = CO2_wanted.Netherlands.reset_index()
NL_CO2_final.columns = ['Year', 'Carbon dioxide emissions, NL']
China_CO2_final = CO2_wanted.China.reset_index()
China_CO2_final.columns = ['Year', 'Carbon dioxide emissions, China']

# Import of the data
filepath5 = 'Data_Sets/Lifeexp.csv'
Lifeexp = pd.read_csv(filepath5, delimiter=',')

# Unuseful columns are removed
Lifeexp_drop = Lifeexp.drop(['Country Code', 'Indicator Name', 'Indicator Code'],  axis=1)
Lifeexp_drop.set_index('Country Name', inplace=True) 
Lifeexp_wanted = Lifeexp_drop.T

# Selection of the wanted data
NL_Lifeexp_final = Lifeexp_wanted.Netherlands.reset_index()
NL_Lifeexp_final.columns = ['Year', 'Life expectancy, NL']
China_Lifeexp_final = Lifeexp_wanted.China.reset_index()
China_Lifeexp_final.columns = ['Year', 'Life expectancy, China']

# Import of the data
filepath6 = 'Data_Sets/GDP.csv'
GPD = pd.read_csv(filepath6, delimiter=',')

# Unuseful columns are removed
GPD_drop = GPD.drop(['Country Code', 'Indicator Name', 'Indicator Code'],  axis=1)
GPD_drop.set_index('Country Name', inplace=True) 
GPD_wanted = GPD_drop.T

# Selection of the wanted data
NL_GPD_final = GPD_wanted.Netherlands.reset_index()
NL_GPD_final.columns = ['Year', 'GDP growth, NL']
China_GPD_final = GPD_wanted.China.reset_index()
China_GPD_final.columns = ['Year', 'GDP growth, China']

#Merging the data on the column 'Year'
df1 = pd.merge(NL_passengers_final, China_passengers_final, how='outer', on='Year')
df1 = pd.merge(df1, NL_freight_final, how='outer', on='Year')
df1 = pd.merge(df1, China_freight_final, how='outer', on='Year')
df1 = pd.merge(df1, NL_container_final, how='outer', on='Year')
df1 = pd.merge(df1, China_container_final, how='outer', on='Year')
df1 = pd.merge(df1, NL_CO2_final, how='outer', on='Year')
df1 = pd.merge(df1, China_CO2_final, how='outer', on='Year')
df2 = pd.merge(China_Lifeexp_final, NL_Lifeexp_final, how='outer', on='Year')
df2 = pd.merge(df2, NL_GPD_final, how='outer', on='Year')
df2 = pd.merge(df2, China_GPD_final, how='outer', on='Year')
df1 = df1.iloc[ 0:61, : ]
df2 = df2.iloc[ 0:61, : ]

#Interactive plot (color makes possible to select on right of graph)
fig = px.line(Covid_China, x='date', y= 'new_cases_smoothed_per_million' ,color='location',
title= 'New covid cases smoothed per million in the Netherlands and in China',
 labels={
                   #Change label names
                   'new_cases_smoothed_per_million': 'New covid cases smoothed per million',
                   'date': 'Date'
               }, ) 

# Add annotations of important moments
fig.add_annotation(x='2020-11-03', y=490, ax='2020-10-29', ay=3000, text='Increase Lockdown',
                    xref='x', yref='y', axref='x', ayref='y')

fig.add_annotation(x='2020-12-14', y=513, ax='2021-01-01', ay=2000, text='Full Lockdown',
                    xref='x', yref='y', axref='x', ayref='y')

fig.add_annotation(x='2021-12-20', y=746, ax='2021-11-25', ay=2500, text='Full Lockdown',
                    xref='x', yref='y', axref='x', ayref='y')

#Show the graph
fig.show()

As the graph above shows, China has indeed recorded very few cases. The Netherlands, on the other hand, has a curve that is dictated by the different epidemic waves. China's curve even seems to be constantly zero. However, they did consider some cases as shown below:

#Interactive plot (color makes possible to select on right of graph)
fig = px.line(Covid_China, x='date', y= 'new_cases_smoothed_per_million' ,color='location',
                range_x= ['2020-01-22', '2020-03-31'], range_y=[0,60], title= 'New covid cases smoothed per million in the Netherlands and in China between January and March 2020',
                 labels={
                   #Change label names
                   'new_cases_smoothed_per_million': 'New covid cases smoothed per million',
                   'date': 'Date'
               }, ) 

#Show the graph
fig.show()

This period is the very beginning of the pandemic. The Netherlands had not even started to register casesHowever, the number of cases remains relatively low compared to the large population of China.

#Interactive plot (color makes possible to select on right of graph)
fig = px.line(Covid_China, x='date', y= 'new_cases_smoothed_per_million' ,color='location',
                range_x= ['2022-04-01', '2022-05-31'], range_y=[0,200], title = 'New covid cases smoothed per million in the Netherlands and in China between April and May 2022',
                labels={
                   #Change label names
                   'new_cases_smoothed_per_million': 'New covid cases smoothed per million',
                   'date': 'Date'
               }, ) 

#Show the graph
fig.show()

As can be seen above, a tiny peak in covid can also be seen in April 2022.

It is interesting to compare the vaccination rate of the population.

#Interactive plot (color makes possible to select on right of graph)
fig = px.line(Covid_China, x='date', y= 'people_fully_vaccinated_per_hundred' ,color='location',
               title= 'Vaccination rate in percent in the Netherlands and in China',
                labels={
                   #Change label names
                   'people_fully_vaccinated_per_hundred': 'People fully vaccinated [%]',
                   'date': 'Date'
               }, ) 

#Show the graph
fig.show()

#Interactive plot (color makes possible to select on right of graph)
fig = px.line(df1, x='Year', y= ['Number of passengers carried, NL', 'Number of passengers carried, China'], title = 'Number of passengers carried',
                labels={
                   #Change label name
                   'value': 'Number of passengers carried'

               }, ) 

#Show the graph
fig.show()

#Interactive plot (color makes possible to select on right of graph)
fig = px.line(df1, x='Year', y= ['Volume of freight carried, NL', 'Volume of freight carried, China'], title = 'Volume of freight carried (in metric tons times kilometers traveled)',
                labels={
                   #Change label name
                   'value': 'Volume of freight carried'

               }, ) 

#Show the graph
fig.show()

#Interactive plot (color makes possible to select on right of graph)
fig = px.line(df1, x='Year', y= ['Flow of containers carried, NL', 'Flow of containers carried, China'], title = 'Flow of containers carried (in twenty-foot equivalent units (TEUs))',
                labels={
                   #Change label name
                   'value': 'Flow of containers carried'

               }, ) 

#Show the graph
fig.show()

#Interactive plot (color makes possible to select on right of graph)
fig = px.line(df1, x='Year', y= ['Carbon dioxide emissions, NL', 'Carbon dioxide emissions, China'], title = 'Carbon dioxide emissions',
                labels={
                   #Change label name
                   'value': 'Carbon dioxide emissions'

               }, ) 

#Show the graph
fig.show()

#Interactive plot (color makes possible to select on right of graph)
fig = px.line(df2, x='Year', y= ['Life expectancy, NL', 'Life expectancy, China'], title = 'Life expectancy',
                labels={
                   #Change label name
                   'value': 'Life expectancy'

               }, ) 

#Show the graph
fig.show()

#Interactive plot (color makes possible to select on right of graph)
fig = px.line(df2, x='Year', y= ['GDP growth, NL', 'GDP growth, China'],  title = 'GDP growth',
                labels={
                   #Change label name
                   'value': 'GDP growth'

               }, ) 

#Show the graph
fig.show()

All these graphs allow us to make several observations:

• With regard to the number of passengers carried, both countries observe a decline when the covid pandemic begins in 2019. It is remarkable that China is experiencing an even greater decline than the Netherlands.

• Regarding the volume of freight carried, it comes to the same conclusion. Both countries observe a decline when the covid pandemic begins in 2019. It is also remarkable that China is experiencing an even greater decline than the Netherlands.

• Flow of containers carried ???

• China's carbon dioxid emissions are on the rise. The appearance of covid has not changed this. In contrast, the Netherlands was already in a phase of decreasing CO2 emissions and the introduction of covid has not changed this trend.

• Life expectancy is falling for the Netherlands. This is due to the additional deaths caused by covid. For China, life expectancy continues to increase in 2019. This is consistent with the low number of covid cases and therefore few (very few) covid deaths.

• Both GDPs has descreased with the covid's introduction. The decrease is somewhat greater for the Netherlands.

Although they have very few cases of Covid, China has generally experienced the same consequences of Covid as the Netherlands. Their economy and the behaviour of the different transport variables are in most cases similar to the behaviour observed in the Netherlands.

To go a step further, it is possible to use the Pearson correlation to better understand the dependencies between the different variables.

# Adding the graph

mask = np.triu(np.ones_like(df1.corr(), dtype=bool))
sns.heatmap(df1.corr(), mask=mask, annot=True)  #annot=True

<AxesSubplot:>

The first observation is that Dutch CO2 emissions are negatively related to other variables. Indeed, the curve is descending while the others are generally ascending. The rest of the variables are positively correlated and are relatively close to 1. This means that they tend to be dependent (the smallest positive value is 0.66).

Conclusion

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